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PersonaFuse: A Personality Activation-Driven Framework for Enhancing Human-LLM Interactions
Yixuan Tang, Yi Yang, Ahmed Abbasi
Recent advancements in Large Language Models (LLMs) demonstrate remarkable capabilities across various fields. These developments have led to more direct communication between huma…
Benchmarking Sociolinguistic Diversity in Swahili NLP: A Taxonomy-Guided Approach
Kezia Oketch, John P. Lalor, Ahmed Abbasi
We introduce the first taxonomy-guided evaluation of Swahili NLP, addressing gaps in sociolinguistic diversity. Drawing on health-related psychometric tasks, we collect a dataset o…
PersonaTwin: A Multi-Tier Prompt Conditioning Framework for Generating and Evaluating Personalized Digital Twins
Sihan Chen, John P. Lalor, Yi Yang +1
While large language models (LLMs) afford new possibilities for user modeling and approximation of human behaviors, they often fail to capture the multidimensional nuances of indiv…
Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed versus Open LLMs for Automated Essay Scoring
Kezia Oketch, John P. Lalor, Yi Yang +1
Closed large language models (LLMs) such as GPT-4 have set state-of-the-art results across a number of NLP tasks and have become central to NLP and machine learning (ML)-driven sol…
Exploring the Relationship between In-Context Learning and Instruction Tuning
Hanyu Duan, Yixuan Tang, Yi Yang +2
In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications. However, they are significantly…
Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads
Yi Yang, Hanyu Duan, Ahmed Abbasi +2
Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks. However, PLMs are prone to encoding stereotypical biase…